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Article
Robust Design Optimization by Polynomial Dimensional Decomposition
Structural and Multidisciplinary Optimization (2013)
  • Xuchun Ren, Georgia Southern University
  • Sharif Rahman, University of Iowa
Abstract
This paper introduces four new methods for robust design optimization (RDO) of complex engineering systems. The methods involve polynomial dimensional decomposition (PDD) of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. New closed-form formulae are presented for the design sensitivities that are simultaneously determined along with the moments. The methods depend on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompassing direct, single-step, sequential, and multi-point single-step design processes. Numerical results indicate that the proposed methods provide accurate and computationally efficient optimal solutions of RDO problems, including an industrial-scale lever arm design.
Keywords
  • Design under uncertainty,
  • ANOVA dimensional decomposition,
  • Orthogonal polynomials,
  • Score functions,
  • Optimization
Publication Date
July, 2013
DOI
10.1007/s00158-013-0883-z
Citation Information
Xuchun Ren and Sharif Rahman. "Robust Design Optimization by Polynomial Dimensional Decomposition" Structural and Multidisciplinary Optimization Vol. 48 Iss. 1 (2013) p. 127 - 148 ISSN: 1615-1488
Available at: http://works.bepress.com/xuchun_ren/3/